Moving window sparse partial least squares method and its application in spectral data

With the advancement of data science and technology, the complexity and diversity of data have increased. Challenges arise when dealing with a larger number of variables than the sample size or the presence of multicollinearity due to strong correlations among variables. In this paper, we propose a moving window sparse partial least squares method that combines the sliding interval technique with sparse partial least squares. By utilizing sliding interval partial least squares regression to identify the optimal interval and incorporating sparse partial least squares for variable selection, the proposed method offers innovations compared to traditional partial least squares (PLS) approaches. Monte Carlo simulations demonstrate its performance in variable selection and model prediction. We apply the method to seawater spectral data, predicting the concentration of chemical Oxygen demand. The results show that the method not only selects reasonable spectral wavelength intervals but also enhances predictive performance.